A framework to estimate a long-term power shortage risk following large-scale earthquake and tsunami disasters

While power shortages during and after a natural disaster cause severe impacts on response and recovery activities, related modeling and data collection efforts have been limited. In particular, no methodology exists to analyze long-term power shortages such as those that occurred during the Great East Japan Earthquake. To visualize a risk of supply shortage during a disaster and assist the coherent recovery of supply and demand systems, this study proposes an integrated damage and recovery estimation framework including the power generator, trunk distribution systems (over 154 kV), and power demand system. This framework is unique because it thoroughly investigates the vulnerability and resilience characteristics of power systems as well as businesses as primary power consumers observed in past disasters in Japan. These characteristics are essentially modeled by statistical functions, and a simple power supply–demand matching algorism is implemented using these functions. As a result, the proposed framework reproduces the original power supply and demand status from the 2011 Great East Japan Earthquake in a relatively consistent manner. Using stochastic components of the statistical functions, the average supply margin is estimated to be 4.1%, but the worst-case scenario is a 5.6% shortfall relative to peak demand. Thus, by applying the framework, the study improves knowledge on potential risk by examining a particular past disaster; the findings are expected to enhance risk perception and supply and demand preparedness after a future large-scale earthquake and tsunami disaster.

generation adequacy and transmission adequacy. The power shortage was calculated as the sum of power generation shortage and route capacity shortage. Regarding supply to the demand nodes, various rules can be considered, such as setting the priority in descending order of importance of the demand nodes (e.g., center of a city) and the order of decreasing transmission loss to the demand nodes.
Here, the importance of the demand node was not considered, but the route distance was calculated considering transmission loss. A demand node was paired with a power generation node with the shortest route distance to the demand node. The shortest distance among all pairs with priority that supply and/or demand was partially or fully determined. Priority of power generation (i.e., which power generation node was selected to meet demand) was empirically determined in our study according to the size, types of generators (e.g., combined cycle), and fuels.
The power flow simulated by this model did not necessarily fit the observed power flows, considering that our aim was to detect bottlenecks associated with supply shortage risks and the amount and duration of shortages over the entire power system. The method is simply matching the demand node with the power generating node using the shortest route under the constraint of link capacity. Many algorithms may be used to find a feasible solution; the following algorithm is relatively simple to implement.
(After simulating power supply capacity and demand recovery at each time period,) [Procedure 1] Select the generator PGtarget that belongs to the power generation node PPtarget whose generation priority is high.
[Procedure 2] For all demand nodes, calculate the shortest route LPPtarget->Di from the PPtarget and its route distance dPPtarget->Di, excluding links damaged or over capacity.
[Procedure 3] Select the node Dtarget with the shortest route distance dPPtarget->Dtarget among all demand nodes where the power supply has not yet met the demand.
[Procedure 4] The power supply capacity CP(PGtarget) of PGtarget, the remaining power demand RD(Dtarget) of Dtarget, and the route capacity CL(LPPtarget->Dtarget) on the supply route LPPtarget->Dtarget are updated according to the following three conditions.  B2. Are there any nodes that connect the node for power plant "PP target " having the PG target without breaking the link due to the damage or excess transmission capacity, and have a gap between supply and demand ?
P2. Search for the shortest route "L PPtarget->Di " and calculate its distance "d PPtarget->Di " from PP target to all nodes having a gap between supply and demand considering broken links due to the damage or excess transmission capacity.
P3. Select the node "D target " to meet supply and demand as a node whose d PPtarget->Di is the minimum of all nodes having the gap between supply and demand.
B3a. Is generating capacity of PG target "CP(PG target ) " greater than remained demand of D target "RD(D target )" ? P4b. D target meets supply and demand partially, while CP(PG target ) and transmission capacity of links on the L PPtarget->Di decrease CL(L PPtarget->Dtarget ).
P4c. Achieved supply from PG target , while RD(D target ) and transmission capacity of links on the L PPtarget->Di decrease CP(PG target ).
Achieved supply from PG target .